Research within the CDT explores how remote sensing and Earth observation can be used to monitor ecosystems, land-use change, and natural capital. Projects use satellite imagery and sensor data to track landscape degradation, vegetation recovery, and the condition of marine and coastal habitats.
AI and machine learning play a key role in improving the prediction and understanding of environmental processes such as air quality, dust transport, extreme weather events, and habitat change.
Work on natural capital and ecosystem function includes modelling carbon dynamics, assessing blue carbon potential, and evaluating the trade-offs between biodiversity conservation and land-use practices.
The CDT fosters strong interdisciplinary collaboration, drawing on expertise in ecology, computer science, geography, and environmental data science. Many projects focus on combining large and complex datasets—from satellites to field sensors—using statistical and computational methods to improve environmental decision-making and sustainability outcomes.